Instructions to use nlpconnect/vit-gpt2-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpconnect/vit-gpt2-image-captioning with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") model = AutoModelForImageTextToText.from_pretrained("nlpconnect/vit-gpt2-image-captioning") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 2aeedcc
link updated
Browse files
README.md
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@@ -21,7 +21,7 @@ This is an image captioning model trained by @ydshieh in [flax ](https://github.
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## https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/
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# Sample running code
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## [https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/](https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/)
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# Sample running code
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